code: def _center_to_corners_format_torch(
      bboxes_center: "torch.Tensor",
  ) -> "torch.Tensor":
      center_x, center_y, width, height = bboxes_center.unbind(-1)
      bbox_corners = torch.stack(
          # top left x, top left y, bottom right x, bottom right y
          [
              (center_x - 0.5 * width),
              (center_y - 0.5 * height),
              (center_x + 0.5 * width),
              (center_y + 0.5 * height),
          ],
          dim=-1,
      )
      return bbox_corners
  
imports: import warnings
  from typing import Iterable, List, Optional, Tuple, Union
  import numpy
  from .image_utils import ChannelDimension, ImageInput, get_channel_dimension_axis, get_image_size, infer_channel_dimension_format
  from .utils import ExplicitEnum, TensorType, is_jax_tensor, is_tf_tensor, is_torch_tensor
  from .utils.import_utils import is_flax_available, is_tf_available, is_torch_available, is_vision_available, requires_backends
label_desc: Use image node_id 9 for calling a global function with example usage: _center_to_corners_format_torch(bboxes_center) and returns: bbox_corners